249 citations · 560 across the 13 of their papers we have counts for
13 papers
Enhancing In-context Learning via Linear Probe Calibration
Momin Abbas, Yi Zhou, Parikshit Ram +4
In-context learning (ICL) is a new paradigm for natural language processing that utilizes Generative Pre-trained Transformer (GPT)-like models. This approach uses prompts that incl…
Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis
Yi Zhou, Parikshit Ram, Theodoros Salonidis +3
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss SuRface Aggregation (FLoRA), a gener…
Selective Edge Computing for Mobile Analytics
Apostolos Galanopoulos, George Iosifidis, Theodoros Salonidis +1
An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves the energy spent on transmitti…
FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning
Yi Zhou, Parikshit Ram, Theodoros Salonidis +3
We address the relatively unexplored problem of hyper-parameter optimization (HPO) for federated learning (FL-HPO). We introduce Federated Loss suRface Aggregation (FLoRA), the fir…
Improving IoT Analytics through Selective Edge Execution
A. Galanopoulos, A. G. Tasiopoulos, G. Iosifidis +2
A large number of emerging IoT applications rely on machine learning routines for analyzing data. Executing such tasks at the user devices improves response time and economizes net…
Anonymizing Data for Privacy-Preserving Federated Learning
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis +4
Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in h…
Differential Privacy-enabled Federated Learning for Sensitive Health Data
Olivia Choudhury, Aris Gkoulalas-Divanis, Theodoros Salonidis +4
Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a central…
Maximum Lifetime Analytics in IoT Networks
Victor Valls, George Iosifidis, Theodoros Salonidis
This paper studies the problem of allocating bandwidth and computation resources to data analytics tasks in Internet of Things (IoT) networks. IoT nodes are powered by batteries, c…
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis +4
Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learnin…
Live Service Migration in Mobile Edge Clouds
Andrew Machen, Shiqiang Wang, Kin K. Leung +2
Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user, by installing small cloud infrastructures at the network edge. This enables a new breed of real-time a…
On the Complexity of Optimal Routing and Content Caching in Heterogeneous Networks
Mostafa Dehghan, Anand Seetharam, Bo Jiang +5
We investigate the problem of optimal request routing and content caching in a heterogeneous network supporting in-network content caching with the goal of minimizing average conte…
Optimal Caching and Routing in Hybrid Networks
Mostafa Dehghan, Anand Seetharam, Ting He +3
Hybrid networks consisting of MANET nodes and cellular infrastructure have been recently proposed to improve the performance of military networks. Prior work has demonstrated the b…
Optimal control of end-user energy storage
Peter M. van de Ven, Nidhi Hegde, Laurent Massoulie +1
An increasing number of retail energy markets show price fluctuations, providing users with the opportunity to buy energy at lower than average prices. We propose to temporarily st…